import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt


#Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist=input_data.read_data_sets("/tmp/data",one_hot=False)


#Visualize decoder setting
#Parameters
learning_rate=0.01
training_epochs=5
batch_size=256
display_step=1
examples_to_show=10


#Network Parameters
n_input=784 #MNIST data input (img shape: 28x28)

#tf Graph input (only pictures)
X=tf.placeholder("float",[None,n_input])


#hidden layer settings
n_hidden_1=256 # 1st layer num features
n_hidden_2=128 # 2nd layer num features
weights={
   'encoder_h1':tf.Variable(tf.random_normal([n_input,n_hidden_1])),
   'encoder_h2':tf.Variable(tf.random_normal([n_hidden_1,n_hidden_2])),

   'decoder_h1':tf.Variable(tf.random_normal([n_hidden_2,n_hidden_1])),
   'decoder_h2':tf.Variable(tf.random_normal([n_hidden_1,n_input])),
}      #dictionary in Python

biases={
   'encoder_b1':tf.Variable(tf.random_normal([n_hidden_1])),
   'encoder_b2':tf.Variable(tf.random_normal([n_hidden_2])),

   'decoder_b1':tf.Variable(tf.random_normal([n_hidden_1])),
   'decoder_b2':tf.Variable(tf.random_normal([n_input])),
}

#Building the encoder
def encoder(x):
    #Encoder Hidden layer with sigmoid activation #1
    layer_1=tf.nn.sigmoid(tf.add(tf.matmul(x,weights['encoder_h1']),
                     biases['encoder_b1']))
    #Encoder Hidden layer with sigmoid activation #2
    layer_2=tf.nn.sigmoid(tf.add(tf.matmul(layer_1,weights['encoder_h2']),
                     biases['encoder_b2']))

    return layer_2

#Building the decoder
def decoder(x):
    #Decoder Hidden layer with sigmoid activation #1
    layer_1=tf.nn.sigmoid(tf.add(tf.matmul(x,weights['decoder_h1']),
                     biases['decoder_b1']))
    #Decoder Hidden layer with sigmoid activation #2
    layer_2=tf.nn.sigmoid(tf.add(tf.matmul(layer_1,weights['decoder_h2']),
                     biases['decoder_b2']))

    return layer_2

#Construct model
encoder_op=encoder(X)
decoder_op=decoder(encoder_op)

#Prediction
y_pred=decoder_op
#Targets(Labels) are the input data.
y_true=X

#Define loss and optimizer, minimize the squared error
cost=tf.reduce_mean(tf.pow(y_true-y_pred,2))
optimizer=tf.train.AdamOptimizer(learning_rate).minimize(cost)

#Initialize the variables
init=tf.initialize_all_variables()


#Launch the graph
with tf.Session() as sess:
     sess.run(init)
     total_batch=int(mnist.train.num_examples/batch_size)
     #Training cycle
     for epoch in range(training_epochs):
         #Loop over all batches
         for i in range(total_batch):
             batch_xs,batch_ys=mnist.train.next_batch(batch_size) #max(x)=1,min(x)
             # Run optimization op (backprop) and cost op (to get loss value)
             _,c=sess.run([optimizer,cost],feed_dict={X:batch_xs})
             #Display logs per epoch step
             if epoch%display_step==0:
                print("Epoch:",'%04d'%(epoch+1),
                      "cost=","{:.9f}".format(c))
     print("Optimization Finished!")
     
     ##Applying encode and decode over test set
     encode_decode=sess.run(

                     y_pred,feed_dict={X:mnist.test.images[:examples_to_show]})
     #Compare original images with their reconstructions
     f,a=plt.subplots(2,10,figsize=(10,2))
     for i in range(examples_to_show):
         a[0][i].imshow(np.reshape(mnist.test.images[i],(28,28)))
         a[1][i].imshow(np.reshape(encode_decode[i],(28,28)))
     plt.show()

